This article reviews the PandaAI framework, a closed‑loop neural‑symbolic LLM agent that models market regimes, applies constrained Monte‑Carlo Tree Search for factor generation, and continuously adapts via back‑test feedback, achieving significantly higher Rank IC and lower drawdown on CSI‑300 data.
The paper introduces FactorEngine, a program‑level factor mining system that injects domain knowledge from financial reports via LLM‑guided pipelines, separates logic correction, parameter optimization, and LLM usage, and demonstrates superior information‑ratio, Sharpe, and annualized returns over GP, neural, and other agent‑based baselines on CSI300 and CSI500 markets.
This article compiles and summarizes recent ICLR2026 papers on quantitative finance, presenting their titles, authors, abstracts, code and paper links, and highlighting benchmarks such as AlphaBench, TiMi, STABLE, and AlphaSAGE that explore large language models and multi‑agent systems for factor mining and trading.
The article reviews AlphaBench, the first benchmark suite for assessing large language models in formalized alpha‑factor mining (FAFM), detailing its three core tasks—factor generation, evaluation, and search—along with experiments on various commercial and open‑source LLMs that reveal strong potential but challenges in robustness, efficiency, and practical usability.
